For decades, military establishments all over the world have used mechanical surrogates of real vehicles as part of simulated environments where human operators are trained to operate vehicles under combat or mission conditions. Perhaps the classic example of such a surrogate is a flight simulator which surrounds the flight crew and emulates the motion and environment of a cock pit. With respect to ground vehicle simulation, much research has been done by the United States Army. In particular, the US Army Tank-Automotive and Armaments Research, Engineering and Development Center (TARDEC) has been performing high-fidelity, real-time, man-in-the-loop simulations for a number of years. My invention is a method of generating a simulated terrain database that enhances TARDEC""s simulation technology.
TARDEC""s simulations are real-time in the sense that a computer""s simulation clock is slaved to actual time, i.e., one second of simulation time on a computer takes one second to complete, no more, no less. TARDEC has integrated high-fidelity motion bases with a computer image generator (an Evans and Sutherland ESIG(copyright)-HD/3000 and a Harmony(copyright) image generator) and an audio generation system (AudioWorks 2(trademark)). This laboratory enhancement allows TARDEC to conduct soldier-in-the-loop testing to analyze the performance of new equipment, and just as importantly, to determine how effectively the soldier can use it in a simulated battlefield environment. TARDEC currently has two six-degree-of-freedom motion-based simulators. One is the Crew Station/Turret Motion Base Simulator (CS/TMBS), which has a bandwidth of ≈10 Hz and a payload capacity of greater than 25 tons. The CS/TMBS is built to handle complete turret systems as well as experimental crew stations. The CS/TMBS has a motion envelope of xc2x130 inches in translational motion (the x, y and z axes) and xc2x118xc2x0 of rotational motion (roll, pitch and yaw). TARDEC""s other motion-based simulator is the Ride Motion Simulator (RMS), a six degree-of-freedom simulator similar to the CS/TMBS but designed for a single crew member. The RMS has a vertical displacement of xc2x120 inches and xc2x110 inches in the horizontal (x and y) directions along with a rotational displacement of xc2x120xc2x0 in roll, pitch and yaw. The RMS also has a bandwidth approaching 40 Hz in the vertical and 20 Hz in x and y. This simulator features an easily reconfigurable cab which will lend itself to quick experimental changes and re-testing. Both simulators"" movements are controlled by real time mathematical models describing the dynamic characteristics of the particular vehicle being tested. These models receive inputs from a human driver (acceleration, steering, etc.) and reproduce accurate motion disturbances that the crew would feel.
TARDEC uses dynamic models in vehicle simulation techniques. A dynamic model is a mathematical representation of the vehicle being simulated running on a real-time computer. It is constructed using computer-based dynamics modeling and simulation algorithms tailored for real-time use. The model accurately incorporates characteristics of the suspension system, wheels or tracks, propulsion, weapon-stabilization system, etc. To accomplish its computer modeling, TARDEC has developed a real-time modeling methodology dubbed Symbolically Optimized Vehicle Analysis System (SOVAS) used to model high-fidelity dynamic systems. SOVAS is a methodology based in traditional C or FORTRAN language constructs and portable to many computer platforms. Currently, the SOVAS models are run over digitized test courses that the Army uses to baseline vehicle systems. These include test areas at the Aberdeen Proving Grounds, Yuma Proving Grounds, Ft. Knox and the Waterways Experiment Station. These courses are digitized to a resolution of 12 inches and are basically a dedicated path, meaning the model is not allowed to wander at will in any direction, but is slaved to a specified path through the test course. In this scenario, the inputs to the SOVAS model are velocity and braking.
The image generators mentioned above use a visual database to provide images to the user. The visual database uses a polygonal grid as its foundation such as that shown in FIG. 1. The grid is usually broken into triangles to guarantee polygonal planarity. To provide realism, the database is populated with various objects (trees, buildings, roads, etc.) and techniques such as Gouraud shading (Phong illumination in the latest image generators) and texture mapping are applied. The image generator""s terrain database is created from terrain-position points and elevations stored in a digital format. The most common method of collecting and storing large terrain areas is to use the Digital Terrain Elevation Data (DTED) format, developed by the United States Government""s Defense Mapping Agency. A limitation of DTED formatting is that it does not provide for terrain sampling at lower than 30 meter intervals. However, all image generators have polygonal output limitations since only so many polygons can be rendered in a frame. A quicker update rate (how fast the frame information changes) lowers the number of polygons that can be displayed. So, even though visual databases can be constructed with finer resolution than 30-meter intervals, rendering these databases is not practical or possible in real time.
TARDEC has incorporated the SOVAS dynamic model with the image generator. This allows for three degree-of-freedom user controlled movement of the model (x, y, yaw), with disturbances being calculated in the remaining degrees-of-freedom (z, roll, pitch). User inputs to the dynamic model now include steering as well as velocity and braking. The terrain the dynamic model is traveling over is the visual terrain database of the image generator. The terrain disturbance inputs to the dynamic model from the image generator are returned in one of two ways: 1) as a height-of-terrain value, or 2) as a planar equation (Ax+By+Cz+D=0) representing the equation of the terrain polygon the dynamic model is currently on.
However, either method is wrought with many problems. Mainly, as mentioned earlier, the resolution of the image generator""s database is very low (between 30 m and 100 m). As an input to the dynamic model, it is equivalent to driving over a flat road. This defeats the purpose of a cross-country simulation, where terrain roughness may range from relatively mild (paved or unpaved road) to severe. Also, this method provides only geometric G0 continuity. This first derivative is not continuous or is even geometrically discontinuous at polygon boundaries as shown at polygonal boundary 2 in FIG. 2. As an input to a mathematical system, this G0 continuity will pose difficulty for mathematical integrators.
One method used to overcome these problems is to have a correlated terrain database sampled at a higher resolution to provide the higher frequency disturbances to the dynamic model. For example, the University of Iowa has developed a Virtual Proving Ground which uses this method, but the principal use of their driving simulator is for on-road conditions. The suspension system of the vehicle is used as a low-pass filter to help overcome the G0 continuity problem. Other methods include simply introducing random high-frequency disturbances into the motion base directly or summing it with the image generator""s database as an input to the dynamic model. While the Iowa method has merit, it has not totally solved the continuity problem. It was also developed at a time when computer power was much less than it is now, so calculations were minimized to a degree that is no longer necessary. Other methods involving random high-frequency disturbances exist and are known, but these methods are non-deterministic and unacceptable.
My invention is a method to create a high-resolution terrain database correlated to a computer image generator""s low-resolution terrain database such as that provided by the ESIG(copyright)-HD/3000 or Harmony(copyright) generators. The high-resolution terrain database is comprised of a multiplicity of high-resolution surface patches that are overlaid upon the generator""s lower-resolution database. The resulting hybrid terrain database has the following characteristics:
1. G1 continuity throughout the entire terrain. G1 continuity occurs when the patches adjoin one another at common boundaries and any two adjoining patches share a common normal vector at each point along their common boundary.
2. Infinite resolution across the terrain database (a continuous surface).
3. Data storage needed to represent this terrain is held to a minimum.
4. The frequency content and rms (root-mean-square) magnitude of the terrain database is user selectable.
Each patch is created using fractional Brownian motion approximated by spectral synthesis. After performing the proper filtering according to a user-defined frequency content range, a 2-D inverse Fast Fourier Transform is used to transform each patch of the surface to the spatial domain. Scaling is then performed to create a surface with the proper magnitude in terms of rms (root-mean-square) value. Then specific data points are extracted from the patches of the high-resolution surface at a sampling rate small enough to properly recreate the highest frequency content specified by the user. These data points are used as the control points in a NURBS (Non-Uniform Rational B-Splines) surface patch. These control points define the rough outline of the surface which lies within a convex hull defined by the control points. This set of control points are then overlaid upon the corresponding lower-fidelity terrain polygons, thus creating the correlated high-resolution terrain database. The correlated database may be regarded as a hybrid terrain database comprised of the image generator""s terrain database modified by surface patches.
To ensure that the newly created terrain surface follows closely to the visual database, the control points are filtered. This gives the newly created surface a vertical offset of zero with respect to the polygonal boundaries of the generator""s lower-fidelity terrain. This means that the high-fidelity surface patches will have the same height as the lower-fidelity surface at the polygon edges of the lower-fidelity surface. In this manner, a terrain database with G2 continuity over most of the database and G0 continuity at polygonal boundaries is created. Finally, the NURBS surface patches are modified at the surface patch boundaries to create G1 continuity among the patches.